Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli.

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

The quantum dot vs. organic dye conundrum for ratiometric FRET-based biosensors: which one would you chose?

Förster resonance energy transfer (FRET) is a widely used and ideal transduction modality for fluorescent based biosensors as it offers high signal to noise with a visibly detectable signal. While intense efforts are ongoing to improve the limit of detection and dynamic range of biosensors based on biomolecule optimization, the selection of and relative location of the dye remains understudied. Herein, we describe a combined experimental and computational study to systematically compare the nature of the dye, i.e., organic fluorophore (Cy5 or Texas Red) vs. inorganic nanoparticle (QD), and the position of the FRET donor or acceptor on the biomolecular components. Using a recently discovered transcription factor (TF)-deoxyribonucleic acid (DNA) biosensor for progesterone, we examine four different biosensor configurations and report the quantum yield, lifetime, FRET efficiency, IC50, and limit of detection. Fitting the computational models to the empirical data identifies key molecular parameters driving sensor performance in each biosensor configuration. Finally, we provide a set of design parameters to enable one to select the fluorophore system for future intermolecular biosensors using FRET-based conformational regulation in in vitro assays and new diagnostic devices.

RegulonDB 11.0: Comprehensive high-throughput datasets on transcriptional regulation in Escherichia coli K-12.

Genomics has set the basis for a variety of methodologies that produce high-throughput datasets identifying the different players that define gene regulation, particularly regulation of transcription initiation and operon organization. These datasets are available in public repositories, such as the Gene Expression Omnibus, or ArrayExpress. However, accessing and navigating such a wealth of data is not straightforward. No resource currently exists that offers all available high and low-throughput data on transcriptional regulation in Escherichia coli K-12 to easily use both as whole datasets, or as individual interactions and regulatory elements. RegulonDB (https://regulondb.ccg.unam.mx) began gathering high-throughput dataset collections in 2009, starting with transcription start sites, then adding ChIP-seq and gSELEX in 2012, with up to 99 different experimental high-throughput datasets available in 2019. In this paper we present a radical upgrade to more than 2000 high-throughput datasets, processed to facilitate their comparison, introducing up-to-date collections of transcription termination sites, transcription units, as well as transcription factor binding interactions derived from ChIP-seq, ChIP-exo, gSELEX and DAP-seq experiments, besides expression profiles derived from RNA-seq experiments. For ChIP-seq experiments we offer both the data as presented by the authors, as well as data uniformly processed in-house, enhancing their comparability, as well as the traceability of the methods and reproducibility of the results. Furthermore, we have expanded the tools available for browsing and visualization across and within datasets. We include comparisons against previously existing knowledge in RegulonDB from classic experiments, a nucleotide-resolution genome viewer, and an interface that enables users to browse datasets by querying their metadata. A particular effort was made to automatically extract detailed experimental growth conditions by implementing an assisted curation strategy applying Natural language processing and machine learning. We provide summaries with the total number of interactions found in each experiment, as well as tools to identify common results among different experiments. This is a long-awaited resource to make use of such wealth of knowledge and advance our understanding of the biology of the model bacterium E. coli K-12.

Development and Clinical Validation of Iso-IMRS: A Novel Diagnostic Assay for P. falciparum Malaria.

In many countries targeting malaria elimination, persistent malaria infections can have parasite loads significantly below the lower limit of detection (LLOD) of standard diagnostic techniques, making them difficult to identify and treat. The most sensitive diagnostic methods involve amplification and detection of Plasmodium DNA by polymerase chain reaction (PCR), which requires expensive thermal cycling equipment and is difficult to deploy in resource-limited settings. Isothermal DNA amplification assays have been developed, but they require complex primer design, resulting in high nonspecific amplification, and show a decrease in sensitivity than PCR methods. Here, we have used a computational approach to design a novel isothermal amplification assay with a simple primer design to amplify P. falciparum DNA with analytical sensitivity comparable to PCR. We have identified short DNA sequences repeated throughout the parasite genome to be used as primers for DNA amplification and demonstrated that these primers can be used, without modification, to isothermally amplify P. falciparum parasite DNA via strand displacement amplification. Our novel assay shows a LLOD of ∼1 parasite/µL within a 30 min amplification time. The assay was demonstrated with clinical samples using patient blood and saliva. We further characterized the assay using direct amplicon next-generation sequencing and modified the assay to work with a visual readout. The technique developed here achieves similar analytical sensitivity to current gold standard PCR assays requiring a fraction of time and resources for PCR. This highly sensitive isothermal assay can be more easily adapted to field settings, making it a potentially useful tool for malaria elimination.

Cell-Type-Specific Analysis of Molecular Pathology in Autism Identifies Common Genes and Pathways Affected Across Neocortical Regions.

Despite its heterogeneity, autism is characterized by a defined behavioral phenotype, suggesting that the molecular pathology affects specific neural substrates to cause behavioral dysfunction. Previous studies identified genes dysregulated in autism cortex but did not address their cell-type specificity. Moreover, it is unknown whether there is a core of genes dysregulated across multiple neocortical regions. We applied RNA sequencing to postmortem brain tissue samples from autism patients and neurologically normal controls and combined our data with previously published datasets. We then identified genes, pathways, and alternative splicing events which are dysregulated in five neocortical regions in autism. To gain information about cell-type specificity of the dysregulated genes, we analyzed single-nuclei RNA sequencing data of adult human cortex and intersected cell-type-specific gene signatures with genes dysregulated in autism in specific cortical regions. We found that autism-associated gene expression changes across 4 frontal and temporal cortex regions converge on 27 genes related to immune response and enriched in human astrocytes, microglia, and brain endothelium. Shared splicing changes, however, are found in genes predominantly associated with synaptic function and adult interneurons and projection neurons. Finally, we demonstrate that regions of DNA differentially methylated in autism overlap genes associated with development and enriched in human cortical oligodendrocytes. Our study identifies signatures of autism molecular pathology shared across neocortical regions, as well as neural cell types enriched for common dysregulated genes, thus paving way for assessing cell-type-specific mechanisms of autism pathology.

CANEapp: a user-friendly application for automated next generation transcriptomic data analysis.

BACKGROUND: Next generation sequencing (NGS) technologies are indispensable for molecular biology research, but data analysis represents the bottleneck in their application. Users need to be familiar with computer terminal commands, the Linux environment, and various software tools and scripts. Analysis workflows have to be optimized and experimentally validated to extract biologically meaningful data. Moreover, as larger datasets are being generated, their analysis requires use of high-performance servers. RESULTS: To address these needs, we developed CANEapp (application for Comprehensive automated Analysis of Next-generation sequencing Experiments), a unique suite that combines a Graphical User Interface (GUI) and an automated server-side analysis pipeline that is platform-independent, making it suitable for any server architecture. The GUI runs on a PC or Mac and seamlessly connects to the server to provide full GUI control of RNA-sequencing (RNA-seq) project analysis. The server-side analysis pipeline contains a framework that is implemented on a Linux server through completely automated installation of software components and reference files. Analysis with CANEapp is also fully automated and performs differential gene expression analysis and novel noncoding RNA discovery through alternative workflows (Cuffdiff and R packages edgeR and DESeq2). We compared CANEapp to other similar tools, and it significantly improves on previous developments. We experimentally validated CANEapp's performance by applying it to data derived from different experimental paradigms and confirming the results with quantitative real-time PCR (qRT-PCR). CANEapp adapts to any server architecture by effectively using available resources and thus handles large amounts of data efficiently. CANEapp performance has been experimentally validated on various biological datasets. CANEapp is available free of charge at http://psychiatry.med.miami.edu/research/laboratory-of-translational-rna-genomics/CANE-app . CONCLUSIONS: We believe that CANEapp will serve both biologists with no computational experience and bioinformaticians as a simple, timesaving but accurate and powerful tool to analyze large RNA-seq datasets and will provide foundations for future development of integrated and automated high-throughput genomics data analysis tools. Due to its inherently standardized pipeline and combination of automated analysis and platform-independence, CANEapp is an ideal for large-scale collaborative RNA-seq projects between different institutions and research groups.